Consumer Behaviors in Pandemic

This page is about trend in personal consumption in the US

TODO:

Change colors;

Add description.

Import data

consumption_product = readxl::read_excel("data/consumption_product.xlsx") %>% 
  janitor::clean_names() %>% 
  pivot_longer(
    x2019_q1 : x2021_q3,
    names_to = "time",
    names_prefix = "x",
    values_to = "consumption"
  ) 

consumption_function = readxl::read_excel("./data/consumption_function.xlsx") %>% 
  janitor::clean_names() %>% 
  filter(as.numeric(line) <= 28) %>% 
    pivot_longer(
    x2019_q1 : x2021_q3,
    names_to = "time",
    names_prefix = "x",
    values_to = "consumption"
  ) %>% 
  mutate(functions = recode(functions, `Household consumption expenditures (for services)` = "household",
                           `Final consumption expenditures of nonprofit institutions serving households (NPISHs)1` = "nonprofit consumption"))

general_1 = consumption_function %>% 
  filter(functions %in% c("Goods","Services"))

covid_seasonal = read_csv("covid_seasonal.csv") %>% 
  rename(time = date) %>% 
  select(time, quarterly)

consumption_seasonal = general_1 %>% 
  select(-line)

covid_consumption = left_join(consumption_seasonal, covid_seasonal, by = "time")

Consumption and Covid

joint_plot = plot_ly(covid_consumption, x = ~time) %>% 
  add_trace(y = ~consumption, type = "scatter", mode = "lines", color = ~functions, yaixs = "y") %>% 
  add_trace(y = ~quarterly, type = "bar", name = "Covid Cases", yaxis = "y2")  %>% 
  layout(title = "Consumption of Goods and Services Compared with Covid Cases",
         yaxis=list(title = "consumption expenditure", side="left"),
         yaxis2=list(title = "covid cases", side="right",overlaying="y"),
         showlegend=TRUE)

joint_plot

Consumption of Goods

general_2 = consumption_function %>% 
  filter(functions %in% c("Durable goods","Nondurable goods","household","nonprofit consumption")) %>% 
  select(-line) %>% 
  pivot_wider(names_from = functions, values_from = consumption) %>% 
  janitor::clean_names()

subfig_1 = plot_ly(general_2, x = ~time, y = ~durable_goods, type = "bar", name = "Durable Goods") %>% 
  add_trace(y = ~nondurable_goods, name = "Nondurable Goods") %>% 
  layout(title = "Decomposition of Consumption of Goods",
         yaxis = list(title = "Consumption"), barmode = "stack",
         legend = list(orientation = 'h', x = 0, y = -0.2))

subfig_1

Durable Goods

durable_goods = 
  consumption_function %>% 
  filter(functions %in% c("Motor vehicles and parts","Furnishings and durable household equipment","Recreational goods and vehicles","Other durable goods")) 

durable_goods %>% 
  plot_ly(x = ~time, y = ~consumption, type = 'scatter', mode = 'lines', yaxis="y", color = ~functions) %>% 
  layout(title = "Decomposition of Consumption in Durable Goods",
         legend = list(orientation = 'h', x = 0, y = -0.2))

Nondurable Goods

nondurable_goods = 
  consumption_function %>% 
  filter(functions %in% c("Food and beverages purchased for off-premises consumption","Clothing and footwear","Gasoline and other energy goods","Other nondurable goods")) 

nondurable_goods %>% 
  plot_ly(x = ~time, y = ~consumption, type = 'scatter', mode = 'lines', yaxis="y", color = ~functions) %>% 
  layout(title = "Decompostion of Consumption in Nondurable Goods",
         legend = list(orientation = 'h', x = 0, y = -0.2))

Consumption of Services

subfig_2 = plot_ly(general_2, x = ~time, y = ~household, type = "bar", name = "Household", colors = "Dark2") %>% 
  add_trace(y = ~nonprofit_consumption, name = "Nonprofit Consumption", colors = "Dark2") %>% 
  layout(title = "Decomposition of Consumption in Services ",
         yaixs = list(title = "Consumption"), barmode = "stack",
         legend = list(orientation = 'h', x = 0, y = -0.2))

subfig_2

Household Services

household_consumption =
  consumption_function %>% 
  filter(functions %in% c("Housing and utilities","Health care","Transportation services","Recreation services","Food services and accommodations","Financial services and insurance","Other services")) 

household_consumption %>% 
  plot_ly(x = ~time, y = ~consumption, type = 'scatter', mode = 'lines', yaxis="y", color = ~functions) %>% 
  layout(title = "Decomposition of Consumption in Household Services",
         legend = list(orientation = 'h', x = 0, y = -0.2))

Consumption Structure Analysis

durable_goods_radar = durable_goods %>% 
  filter(time %in% c("2019_q1", "2021_q3")) %>% 
  select(functions, time, consumption)  %>% 
  pivot_wider(names_from = functions, values_from = consumption) 

total = rep(1,5)
start = rep(0,5)
durable_goods_radar = rbind(total, start, durable_goods_radar)[,-1]
rownames(durable_goods_radar) = c("1", "2", "2019_q1", "2021_q3")
colnames(durable_goods_radar) = c("motor", "furnishing", "recreation", "other")
durable_goods_radar[3,] = durable_goods_radar[3,]/1473292
durable_goods_radar[4,] = durable_goods_radar[4,]/1984391

durable_goods_radar[-c(1,2),] %>% 
  knitr::kable()

motor	furnishing	recreation	other
0.3394256	0.2387931	0.2761163	0.1456649
0.3303764	0.2366091	0.2889768	0.1440371

radar_1 = radarchart(durable_goods_radar) 

nondurable_goods_radar = nondurable_goods %>% 
  filter(time %in% c("2019_q1", "2021_q3")) %>% 
  select(functions, time, consumption)  %>% 
  pivot_wider(names_from = functions, values_from = consumption) 

nondurable_goods_radar = rbind(total, start, nondurable_goods_radar)[,-1]
rownames(nondurable_goods_radar) = c("1", "2", "2019_q1", "2021_q3")
colnames(nondurable_goods_radar) = c("food/beverage", "clothing", "energy", "other")
nondurable_goods_radar[3,] = nondurable_goods_radar[3,]/2909515
nondurable_goods_radar[4,] = nondurable_goods_radar[4,]/3509766

nondurable_goods_radar[-c(1,2),] %>% 
  knitr::kable()

food/beverage	clothing	energy	other
0.3483196	0.1351971	0.1113089	0.4051741
0.3551200	0.1367587	0.1078300	0.4002914

radar_2 = radarchart(nondurable_goods_radar)

household_consumption_radar = household_consumption %>% 
  filter(time %in% c("2019_q1", "2021_q3")) %>% 
  select(functions, time, consumption)  %>% 
  pivot_wider(names_from = functions, values_from = consumption) 

total = rep(1,8)
start = rep(0,8)
household_consumption_radar = rbind(total, start, household_consumption_radar)[,-1]
rownames(household_consumption_radar) = c("1", "2", "2019_q1", "2021_q3")
colnames(household_consumption_radar) = c("housing", "healthcare", "transports", "recreation", "accomodation", "finance", "other")
household_consumption_radar[3,] = household_consumption_radar[3,]/9336650
household_consumption_radar[4,] = household_consumption_radar[4,]/9984527

household_consumption_radar[-c(1,2),] %>% 
  knitr::kable()

housing	healthcare	transports	recreation	accomodation	finance	other
0.2714813	0.2579861	0.0513708	0.0614477	0.1057028	0.1238437	0.1281675
0.2793373	0.2583677	0.0461719	0.0516284	0.1083851	0.1279287	0.1281807

radar_3 = radarchart(household_consumption_radar)